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import os, types |
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import json |
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from enum import Enum |
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import requests |
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import time |
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from typing import Callable, Optional |
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import litellm |
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from litellm.utils import ModelResponse, Usage |
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from .prompt_templates.factory import prompt_factory, custom_prompt |
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class PetalsError(Exception): |
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def __init__(self, status_code, message): |
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self.status_code = status_code |
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self.message = message |
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super().__init__( |
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self.message |
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) |
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class PetalsConfig(): |
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""" |
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Reference: https://github.com/petals-infra/chat.petals.dev#post-apiv1generate |
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The `PetalsConfig` class encapsulates the configuration for the Petals API. The properties of this class are described below: |
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- `max_length` (integer): This represents the maximum length of the generated text (including the prefix) in tokens. |
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- `max_new_tokens` (integer): This represents the maximum number of newly generated tokens (excluding the prefix). |
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The generation parameters are compatible with `.generate()` from Hugging Face's Transformers library: |
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- `do_sample` (boolean, optional): If set to 0 (default), the API runs greedy generation. If set to 1, the API performs sampling using the parameters below: |
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- `temperature` (float, optional): This value sets the temperature for sampling. |
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- `top_k` (integer, optional): This value sets the limit for top-k sampling. |
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- `top_p` (float, optional): This value sets the limit for top-p (nucleus) sampling. |
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- `repetition_penalty` (float, optional): This helps apply the repetition penalty during text generation, as discussed in this paper. |
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""" |
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max_length: Optional[int]=None |
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max_new_tokens: Optional[int]=litellm.max_tokens |
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do_sample: Optional[bool]=None |
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temperature: Optional[float]=None |
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top_k: Optional[int]=None |
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top_p: Optional[float]=None |
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repetition_penalty: Optional[float]=None |
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def __init__(self, |
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max_length: Optional[int]=None, |
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max_new_tokens: Optional[int]=litellm.max_tokens, |
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do_sample: Optional[bool]=None, |
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temperature: Optional[float]=None, |
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top_k: Optional[int]=None, |
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top_p: Optional[float]=None, |
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repetition_penalty: Optional[float]=None) -> None: |
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locals_ = locals() |
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for key, value in locals_.items(): |
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if key != 'self' and value is not None: |
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setattr(self.__class__, key, value) |
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@classmethod |
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def get_config(cls): |
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return {k: v for k, v in cls.__dict__.items() |
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if not k.startswith('__') |
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and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) |
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and v is not None} |
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def completion( |
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model: str, |
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messages: list, |
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api_base: Optional[str], |
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model_response: ModelResponse, |
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print_verbose: Callable, |
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encoding, |
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logging_obj, |
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optional_params=None, |
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stream=False, |
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litellm_params=None, |
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logger_fn=None, |
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): |
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config = litellm.PetalsConfig.get_config() |
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for k, v in config.items(): |
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if k not in optional_params: |
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optional_params[k] = v |
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if model in litellm.custom_prompt_dict: |
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model_prompt_details = litellm.custom_prompt_dict[model] |
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prompt = custom_prompt( |
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role_dict=model_prompt_details["roles"], |
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initial_prompt_value=model_prompt_details["initial_prompt_value"], |
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final_prompt_value=model_prompt_details["final_prompt_value"], |
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messages=messages |
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) |
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else: |
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prompt = prompt_factory(model=model, messages=messages) |
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if api_base: |
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logging_obj.pre_call( |
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input=prompt, |
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api_key="", |
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additional_args={"complete_input_dict": optional_params, "api_base": api_base}, |
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) |
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data = { |
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"model": model, |
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"inputs": prompt, |
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**optional_params |
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} |
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response = requests.post(api_base, data=data) |
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logging_obj.post_call( |
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input=prompt, |
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api_key="", |
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original_response=response.text, |
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additional_args={"complete_input_dict": optional_params}, |
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) |
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try: |
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output_text = response.json()["outputs"] |
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except Exception as e: |
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PetalsError(status_code=response.status_code, message=str(e)) |
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else: |
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try: |
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import torch |
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from transformers import AutoTokenizer |
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from petals import AutoDistributedModelForCausalLM |
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except: |
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raise Exception( |
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"Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals" |
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) |
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model = model |
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tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, add_bos_token=False) |
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model_obj = AutoDistributedModelForCausalLM.from_pretrained(model) |
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logging_obj.pre_call( |
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input=prompt, |
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api_key="", |
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additional_args={"complete_input_dict": optional_params}, |
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) |
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inputs = tokenizer(prompt, return_tensors="pt")["input_ids"] |
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outputs = model_obj.generate(inputs, **optional_params) |
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logging_obj.post_call( |
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input=prompt, |
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api_key="", |
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original_response=outputs, |
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additional_args={"complete_input_dict": optional_params}, |
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) |
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output_text = tokenizer.decode(outputs[0]) |
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if len(output_text) > 0: |
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model_response["choices"][0]["message"]["content"] = output_text |
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prompt_tokens = len( |
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encoding.encode(prompt) |
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) |
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completion_tokens = len( |
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encoding.encode(model_response["choices"][0]["message"].get("content")) |
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) |
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model_response["created"] = int(time.time()) |
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model_response["model"] = model |
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usage = Usage( |
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prompt_tokens=prompt_tokens, |
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completion_tokens=completion_tokens, |
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total_tokens=prompt_tokens + completion_tokens |
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) |
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model_response.usage = usage |
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return model_response |
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def embedding(): |
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pass |
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